@conference {2026,
	title = {Emerging AI Technologies for Music: Towards Controllable, Collaborative, and Creative Systems},
	booktitle = {Proceedings of Machine Learning Research, PMLR 303:1-5, 2026},
	year = {2026},
	abstract = {The field of AI and music has witnessed rapid growth in recent years driving notable success across domains such as music information retrieval (MIR), generative modelling, representation learning, source separation, and transcription among several others. The emergence of large-scale music foundation models (e.g., MusicLM, Suno, Riffusion) and multimodal systems combining modalities such as text, audio and video has further expanded the creative possibilities of AI-driven music. However, despite these technical advances, there remains a significant gap between the capabilities of current AI music systems and their practical adoption by musicians, composers, producers, and educators. At the same time, there has been a growing consensus in the community that recognizes the limitations of fully autonomous generation and the need for interactive, interpretable, controllable, and human-centred approaches. In light of this emerging trend to maintain meaningful human agency over the creative process, the editors of this special issue organized the first workshop on Emerging AI Technologies for Music, as part of the Association for the Advancement of Artificial Intelligence (AAAI) conference in 2026 at Singapore.

Full proceedings: https://proceedings.mlr.press/v303/},
	author = {K. Bhandari and A. Roy and S. Colton and D. Herremans}
}
@conference {2026,
	title = {Text2Score: Generating Sheet Music From Textual Prompts},
	booktitle = {arXiv:2605.13431},
	year = {2026},
	abstract = {Developing text-driven symbolic music generation models remains challenging due to the scarcity of aligned text-music datasets and the unreliability of automated captioning pipelines. While most efforts have focused on MIDI, sheet music representations are largely underexplored in text-driven generation. We present Text2Score, a two-stage framework comprising a planning stage and an execution stage for generating sheet music from natural language prompts. By deriving supervision signals directly from symbolic XML data, we propose an alternative training paradigm that bypasses noisy or scarce text-music pairs. In the planning stage, an LLM orchestrator translates a natural language prompt into a structured measure-wise plan defining musical attributes such as instruments, key, time signatures, harmony, etc. This plan is then consumed by a generative model in the execution stage to produce interleaved ABC notation conditioned on the plan{\textquoteright}s structural constraints. To assess output quality, we introduce an evaluation framework covering playability, readability, instrument utilization, structural complexity, and prompt adherence, validated by expert musicians. Text2Score consistently outperforms both a pure LLM-based agentic framework and three end-to-end baselines across objective and subjective dimensions. We open-source the dataset, code, evaluation set and LLM prompts used in this work; a demo is available on our project page (https://keshavbhandari.github.io/portfolio/text2score).},
	url = {https://arxiv.org/abs/2605.13431},
	author = {K. Bhandari and S. Chang and A. Roy and F. Ronchini and E. Benetos and D. Herremans and S. Colton}
}
@conference {2025,
	title = {HHNAS-AM: Hierarchical Hybrid Neural Architecture Search using Adaptive Mutation Policies},
	booktitle = {arXiv:2508.14946},
	year = {2025},
	abstract = {Neural Architecture Search (NAS) has garnered significant research interest due to its capability to discover architectures superior to manually designed ones. Learning text representation is crucial for text classification and other language-related tasks. The NAS model used in text classification does not have a Hybrid hierarchical structure, and there is no restriction on the architecture structure, due to which the search space becomes very large and mostly redundant, so the existing RL models are not able to navigate the search space effectively. Also, doing a flat architecture search leads to an unorganised search space, which is difficult to traverse. For this purpose, we propose HHNAS-AM (Hierarchical Hybrid Neural Architecture Search with Adaptive Mutation Policies), a novel approach that efficiently explores diverse architectural configurations. We introduce a few architectural templates to search on which organise the search spaces, where search spaces are designed on the basis of domain-specific cues. Our method employs mutation strategies that dynamically adapt based on performance feedback from previous iterations using Q-learning, enabling a more effective and accelerated traversal of the search space. The proposed model is fully probabilistic, enabling effective exploration of the search space. We evaluate our approach on the database id (db_id) prediction task, where it consistently discovers high-performing architectures across multiple experiments. On the Spider dataset, our method achieves an 8\% improvement in test accuracy over existing baselines.},
	url = {https://arxiv.org/abs/2508.14946},
	author = {A. Tripathi and A. Kumar Singh and R. Surya and A. Gupta and S.L. Veikho and D. Herremans and S. Bisane}
}
@conference {2025,
	title = {ImprovNet: Generating Controllable Musical Improvisations with Iterative Corruption Refinement},
	booktitle = {Proceedings of IJCNN},
	year = {2025},
	address = {Rome, Italy},
	abstract = {Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model{\textquoteright}s iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet{\textquoteright}s effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79\% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at this https URL (https://github.com/keshavbhandari/improvnet).},
	doi = {10.1109/IJCNN64981.2025.11229312},
	url = {https://arxiv.org/abs/2502.04522},
	author = {K. Bhandari and S. Chang and T. Lu and F. R. Enus and L. B. Bradshaw and D. Herremans and S. Colton}
}
@article {2024,
	title = {Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey},
	journal = {ACM Computing Surveys},
	year = {2025},
	abstract = {Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.},
	doi = {https://arxiv.org/abs/2402.17467},
	url = {https://arxiv.org/abs/2402.17467},
	author = {Dinh-Viet-Toan Le and L. Bigo and M. Keller and D. Herremans}
}
@article {2025,
	title = {Text2midi: Generating Symbolic Music from Captions},
	journal = {Proceedings of AAAI, Philadelphia},
	year = {2025},
	abstract = {This paper introduces text2midi, an end-to-end model to generate MIDI files from textual descriptions. Leveraging the growing popularity of multimodal generative approaches, text2midi capitalizes on the extensive availability of textual data and the success of large language models (LLMs). Our end-to-end system harnesses the power of LLMs to generate symbolic music in the form of MIDI files. Specifically, we utilize a pretrained LLM encoder to process captions, which then condition an autoregressive transformer decoder to produce MIDI sequences that accurately reflect the provided descriptions. This intuitive and user-friendly method significantly streamlines the music creation process by allowing users to generate music pieces using text prompts. We conduct comprehensive empirical evaluations, incorporating both automated and human studies, that show our model generates MIDI files of high quality that are indeed controllable by text captions that may include music theory terms such as chords, keys, and tempo. We release the code and music samples on our demo page (this https URL) for users to interact with text2midi.},
	url = {https://www.arxiv.org/abs/2412.16526},
	author = {K. Bhandari and A. Roy and K. Wang and G. Puri and S. Colton and D. Herremans}
}
@conference {2022,
	title = {Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps},
	booktitle = {2022 International Conference on Research in Adaptive and Convergent Systems},
	year = {2022},
	publisher = {2022},
	organization = {2022},
	address = {Virtual},
	doi = {10.1145/3538641.3561501},
	url = {https://dl.acm.org/doi/abs/10.1145/3538641.3561501},
	author = {C.J. Clarke and J. Chowdhury and Balamurali BT and P. Priyadarshinee and C.M. Ying Lim and I. Fu Xing Tan and D. Herremans and J.M. Chen}
}
@article {2022,
	title = {Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia},
	journal = {Egusphere preprint},
	year = {2022},
	abstract = {Inspired by recent advancements in the field of computer vision, specifically models for generating higher-resolution images from low-resolution images, we investigate the utility of a deep convolutional autoencoder for downscaling and bias correcting climate projections for South East Asia (SEA). Downscaled projections of 2 m surface temperature are generated, using autoencoders trained with data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and data from the fifth generation ECMWF atmospheric reanalysis (ERA5) project. Using CMIP5 projections as an input, three sets of downscaled data are generated using three methods of autoencoder training, which allow us to determine how autoencoder downscaling and bias correction modify temperature values. Where possible, the downscaled outputs are compared against the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment{\textendash}Southeast Asia (SEACLID/CORDEX{\textendash}SEA) project and outputs from available CMIP6 experiments, to evaluate performance. The autoencoders are found to excel at the rapid generation of highly spatially-resolved climate projections for surface temperature. Realistic spatial features due to coastal and topographic variation are generated by the autoencoder, which are not present in the CMIP5 projections. Additionally, the autoencoders are capable of generating forecast data with regional temperature profiles exceeding that of those appearing in the training set (out-of-sample extrapolation). Seasonal temperature cycles are retained after downscaling throughout the region, despite the absence of temporal information provided to the model. However, autoencoders trained to carry out bias correction display a tendency to smooth daily average temperatures and reduce daily highs and lows beyond that which can be expected to be realistic. Without bias correction, downscaled outputs have a reduced improvement in spatial resolution but the daily temperature profiles of the CMIP5 input forecasts are maintained. Autoencoders rely on the presence of structural features in the datasets to carry out downscaling, and so performance over the oceans is reduced as strong temperature gradients are absent. For this reason, ocean warming is not well represented, an artefact which is not immediately clear in the downscaled outputs. This study demonstrates the importance of rigorous analysis of {\textquoteright}black-box{\textquoteright} methods, which can generate non-obvious artefacts that could potentially create misleading results. Despite these limitations, Autoencoders are clearly capable of generating much needed high-resolution climate projections, and strategies to improve upon shortcomings are numerous and well established. },
	doi = {https://doi.org/10.5194/egusphere-2022-234},
	url = {https://egusphere.copernicus.org/preprints/2022/egusphere-2022-234/},
	author = {O. D. Levers and D. Herremans and A. Dipankar and L. Blessing}
}
@conference {2022,
	title = {A Gaussian mixture classifier model to differentiate respiratory symptoms using phonated /ɑ:/ sounds},
	booktitle = {The 18th Australasian International Conference on Speech Science and Technology (SST)},
	year = {2022},
	month = {12/2022},
	publisher = {ASSTA},
	organization = {ASSTA},
	address = {Canberra, Australia},
	abstract = {An audio-based classification model that differentiates between healthy vs pathological respiratory symptoms using acoustic features extracted from phonated /ɑ:/ sounds is presented. For this, a new dataset of phonated /ɑ:/ sounds, together with a clinician{\textquoteright}s diagnosis, was compiled and a Gaussian Mixture Model (GMM) using Mel-Frequency Cepstral Coefficients (MFCCs) classifier was used. Despite no significant differences in mean values of the fundamental and formant frequency (F0, F1, F2, and F3) distribution for /ɑ:/ sounds retrieved from healthy vs pathological populations, our /ɑ:/ sound model trained using MFCCs resulted in an accuracy of 81.92\% when compared against clinician{\textquoteright}s diagnosis.},
	url = {https://sst2022.com/a-gaussian-mixture-classifier-model-to-differentiate-respiratory-symptoms-using-phonated-a\%cb\%90-sounds/},
	author = {Balamurali BT and H.I. Hee and C. Ming and Y. Lin and P. Priyadarshinee and C.J. Clarke and D. Herremans and J.M. Chen}
}
@conference {2022,
	title = {HEAR 2021: Holistic Evaluation of Audio Representations},
	booktitle = {Proceedings of Machine Learning Research (PMLR): NeurIPS 2021 Competition Track},
	year = {2022},
	abstract = {What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.},
	url = {https://arxiv.org/abs/2203.03022},
	author = {Joseph Turian and Jordie Shier and Humair Raj Khan and Bhiksha Raj and Bj{\"o}rn W. Schuller and Christian J. Steinmetz and Colin Malloy and George Tzanetakis and Gissel Velarde and Kirk McNally and Max Henry and Nicolas Pinto and Camille Noufi and Christian Clough and D. Herremans and Eduardo Fonseca and Jesse Engel and Justin Salamon and Philippe Esling and Pranay Manocha and Shinji Watanabe and Zeyu Jin and Yonatan Bisk}
}
@conference {2022,
	title = {A Machine Learning Approach for MIDI to Guitar Tablature Conversion},
	booktitle = {Sound and Music Computing Conference (SMC)},
	year = {2022},
	author = {N. Kaliakatsos-Papakostas and G. Bastas and D. Makris and D. Herremans and V. Katsouros and P. Maragos}
}
@article {2021,
	title = {AttendAffectNet {\textendash} Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-attention},
	journal = {Sensors. Special issue on Intelligent Sensors: Sensor Based Multi-Modal Emotion Recognition},
	year = {2021},
	abstract = {In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, those typically , while ignoring the correlation between multiple modality inputs nor the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture namely AttendAffectNet (AAN), which uses the self-attention mechanism for predicting emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are taken into account for predicting emotions (expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie excerpt, so as to capture the relationship between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, the self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying the self-attention first on vectors of features obtained from different modalities in each movie segment, and then on the feature representations of all subsequent (temporal) movie segments. We extensively train and validate our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs perform better than those using features extracted from each modality separately. In addition, the Feature AAN outperforms other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performs better than the baseline models when predicting the valence dimension.},
	doi = {10.3390/s21248356},
	url = {https://www.mdpi.com/1424-8220/21/24/8356},
	author = {T. Ha Thi Phuong and Balamurali BT and G. Roig and D. Herremans}
}
@conference {2021,
	title = {AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies},
	booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR2020)},
	year = {2021},
	month = {01/2021},
	address = {Milano, Italy (virtual)},
	author = {T. Ha Thi Phuong and Balamurali BT and D. Herremans and G. Roig}
}
@conference {2020,
	title = {The Effect of Spectrogram Reconstructions on Automatic Music Transcription:An Alternative Approach to Improve Transcription Accuracy},
	booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR2020)},
	year = {2021},
	month = {01/2021},
	address = {Milano, Italy (virtual)},
	author = {K.W. Cheuk and Y.J. Luo and E. Benetos and D. Herremans}
}
@article {2021,
	title = {Evaluating the Effectiveness of an Augmented Reality Game Promoting Environmental Action},
	journal = {Sustainability},
	volume = {13},
	year = {2021},
	pages = {13912},
	abstract = {While public awareness of climate change has grown over the years, many people still have misconceptions regarding effective individual environmental action. In this paper, we present a serious game called PEAR, developed using elements of geolocation and augmented reality (AR), aimed at increasing players{\textquoteright} awareness of climate change issues and propensity for effective sustainable behaviours. We conducted a study with participants who played the game, gauging their knowledge of and attitudes towards climate change issues before and after playing the game. Our results show that the game significantly improved participants{\textquoteright} knowledge on sustainability and climate-change-related issues, and that it also significantly improved their attitudes towards these topics, thus proving that serious games have the potential to impart knowledge and promote sustainable behaviours. Additionally, our results address the lack of empirical studies on the knowledge base of serious sustainability games by introducing methods of quantitatively analysing the effects of serious sustainability games while additionally providing more knowledge about the effectiveness of the specific design elements of our game.},
	doi = {10.3390/su132413912},
	url = {https://www.mdpi.com/2071-1050/13/24/13912},
	author = {K. Wang and Z. Tekler and L. Cheah and D. Herremans and L. Blessing}
}
@article {2021,
	title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for healthcare and well-being},
	journal = {Music \& Science},
	year = {2021},
	abstract = {The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop {\textquotedblleft}Music, Computing, and Health{\textquotedblright} was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.},
	doi = {https://doi.org/10.1177/205920432199770},
	url = {https://journals.sagepub.com/doi/full/10.1177/2059204321997709},
	author = {K. Agres and Schaefer, Rebecca and Volk, Anja and Van Hooren, Susan and Holzapfel, Andr{\'e} and Dalla Bella, Simone and M{\"u}ller, Meinard and de Witte, Martina and D. Herremans and Ramirez Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy}
}
@conference {2021,
	title = {Revisiting the Onsets and Frames Model with Additive Attention},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Virtual},
	author = {K.W. Cheuk and Y.J. Luo and E. Benetos and D. Herremans}
}
@conference {2020,
	title = {Acoustic prediction of flowrate: varying liquid jet stream onto a free surface},
	booktitle = {IEEE International Conference on Signal Processing and Communications (SPCOM)},
	year = {2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Blangladesh, India},
	abstract = {Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passive free surface. In this investigation, we collect the sound of a water jet of varying flowrate falling into a pool of water, and use this sound to predict the flowrate and flowrate trajectory involved. Two approaches are employed: one uses machine-learning models trained using audio features extracted from the collected sound to predict the flowrate (and subsequently the flowrate trajectory). In contrast, the second method directly uses acoustic parameters related to the spectral energy of the liquid-liquid interaction to estimate the flowrate trajectory. The actual flowrate, however, is determined directly using a gravimetric method: tracking the change in mass of the pooling liquid over time. We show here that the two methods agree well with the actual flowrate and offer comparable performance in accurately predicting the flowrate trajectory, and accordingly offer insights for potential real-life applications using sound.},
	author = {Balamurali BT and E. J. Aslim and Yun Shu Lynn Ng and Tricia Li Chuen Kuo and Jacob Shihang Chen and D. Herremans and Lay Guat Ng and J.M. Chen}
}
@article {2020,
	title = {Asthmatic versus healthy child classification based on cough and vocalised /a:/ sounds},
	journal = {The Journal of the Acoustical Society of America (JASA)},
	volume = {148, EL253},
	year = {2020},
	month = {09/2020},
	type = {Jasa Express Letters},
	abstract = {Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician{\textquoteright}s diagnosis, this cough sound model reaches an overall accuracy of 95.3\%. The vocalised /ɑ:/ model reaches an accuracy of 72.2\%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.
},
	doi = {10.1121/10.0001933},
	url = {https://asa.scitation.org/doi/10.1121/10.0001933},
	author = {Balamurali BT and H.I. Hee and O.H. Teoh and K.P. Lee and S. Kapoor and D. Herremans and J.M. Chen}
}
@conference {2020,
	title = {A dataset and classification model for Malay, Hindi, Tamil and Chinese music},
	booktitle = {13th Workshop on music and machine learning (MML) as part of ECML/PKDD},
	year = {2020},
	month = {08/2020},
	abstract = {In this paper we present a new dataset, with musical excepts from the three main ethnic groups in Singapore: Chinese, Malay and Indian (both Hindi and Tamil). We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups. The classification models were optimized by exploring the use of different musical features as the input. Both high level features, i.e., musically meaningful features, as well as low level features, i.e., spectrogram based features, were extracted from the audio files so as to optimize the performance of the different classification models.},
	author = {F. Nahar and K. Agres and Balamurali BT and D. Herremans}
}
@conference {2020,
	title = {Regression-based music emotion prediction using triplet neural networks},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2020},
	month = {07/2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Glasgow},
	abstract = {In this paper, we adapt triplet neural networks (TNNs) to a regression task, music emotion prediction. Since TNNs were initially introduced for classification, and not for regression, we propose a mechanism that allows them to provide meaningful low dimensional representations for regression tasks. We then use these new representations as the input for regression algorithms such as support vector machines and gradient boosting machines. To demonstrate the TNNs{\textquoteright} effectiveness at creating meaningful representations, we compare them to different dimensionality reduction methods on music emotion prediction, i.e., predicting valence and arousal values from musical audio signals. Our results on the DEAM dataset show that by using TNNs we achieve 90\% feature dimensionality reduction with a 9\% improvement in valence prediction and 4\% improvement in arousal prediction with respect to our baseline models (without TNN). Our TNN method outperforms other dimensionality reduction methods such as principal component analysis (PCA) and autoencoders (AE). This shows that, in addition to providing a compact latent space representation of audio features, the proposed approach has a relatively high performance over the baseline models.},
	url = {https://arxiv.org/abs/2001.09988},
	author = {K.W. Cheuk and Y.J. Luo and Balamurali BT and G. Roig and D. Herremans}
}
@article {9998,
	title = {Development of Machine Learning for asthmatic and healthy voluntary  cough - a proof of concept study},
	journal = {Applied Sciences},
	volume = {9},
	year = {2019},
	month = {07/2019},
	chapter = {2833},
	abstract = {(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children{\textquoteright}s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts{\textemdash}healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model{\textendash}Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians{\textquoteright} labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81\% and 84.76\%, respectively, when differentiating coughs from asthmatic children versus coughs from {\textquoteleft}healthy{\textquoteright} children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.},
	doi = {10.3390/app9142833},
	url = {https://www.mdpi.com/2076-3417/9/14/2833},
	author = {H.I. Hee and Balamurali BT and A. Karunakaran and D. Herremans and O.H. Teoh and K.P. Lee and S.S. Teng and S. Lui and J.M. Chen}
}
@inbook {2019,
	title = {The impact of musical structure on enjoyment and absorptive listening states in trance music},
	booktitle = {Music and Consciousness 2 - Worlds, Practices, Modalities},
	year = {2019},
	publisher = {Oxford University Press},
	organization = {Oxford University Press},
	edition = {Editors: Ruth Herbert, David Clarke, and Eric Clarke},
	address = {Oxfort, UK},
	abstract = {https://books.google.com.sg/books?id=DCOQDwAAQBAJ\&pg=PA254\&lpg=PA254\&dq=The+impact+of+musical+structure+on+enjoyment+and+absorptive+listening+states+in+trance+music\&source=bl\&ots=FsWlNXsys-\&sig=ACfU3U0K6dFJD7zbC917zG6HRFXLxVSdvQ\&hl=en\&sa=X\&ved=2ahUKEwjd05e7jdXkAhU_4nMBHe7bBQgQ6AEwBnoECAkQAQ$\#$v=onepage\&q=The\%20impact\%20of\%20musical\%20structure\%20on\%20enjoyment\%20and\%20absorptive\%20listening\%20states\%20in\%20trance\%20music\&f=false},
	issn = {9780198804352},
	url = {https://global.oup.com/academic/product/music-and-consciousness-2-9780198804352?cc=us\&lang=en\&$\#$},
	author = {K. Agres and L. Bigo and D. Herremans}
}
@conference {2019,
	title = {Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks},
	booktitle = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019)},
	year = {2019},
	month = {12/2019},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Sentosa, Singapore},
	author = {K.W. Cheuk and Balamurali BT and G. Roig and D. Herremans}
}
@article {122,
	title = {Machine Learning Research that Matters for Music  Creation: A Case Study},
	journal = {Journal of New Music Research},
	volume = {48},
	year = {2019},
	pages = { 36-55},
	abstract = {Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.},
	doi = {10.1080/09298215.2018.1515233},
	author = {B. Sturm and O. Ben-Tal and U. Monaghan and N. Collins and D. Herremans and E. Chew and G. Hadjeres and E. Deruty and F. Pachet}
}
@article {2019,
	title = {Towards robust audio spoofing detection: a detailed comparison of traditional and learned features},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	pages = {84229 - 84241},
	doi = {10.1109/ACCESS.2019.2923806},
	url = {https://arxiv.org/abs/1905.12439},
	author = {Balamurali BT and K.W.E. Lin and S. Lui and J.M. Chen and D. Herremans}
}
@article {129,
	title = {Blacklisted speaker identification using triplet neural networks},
	year = {2018},
	url = {http://mce.csail.mit.edu/pdfs/SUTD_description.pdf},
	author = {K.W. Cheuk and Balamurali BT and G. Roig and D. Herremans}
}
@article {134,
	title = {Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy},
	journal = {Neural Computing and Applications},
	year = {2018},
	abstract = {Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre-and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 \~{} 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. An experiment with the DSD100 dataset on the full-tracks song evaluation task also shows that our model is able to compete with cutting-edge singing voice separation systems which use multichannel modeling, data augmentation, and model blending.},
	doi = {10.1007/s00521-018-3933-z},
	url = {https://rdcu.be/bdoTY},
	author = {K.W.E. Lin and Balamurali BT and E. Koh and S. Lui and D. Herremans}
}
@article {77,
	title = {Harmonic Structure Predicts the Enjoyment of  Uplifting Trance Music},
	journal = {Frontiers in Psychology, Cognitive Science},
	volume = {7},
	year = {2017},
	url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.01999/full?\&utm_source=Email_to_authors_\&utm_medium=Email\&utm_content=T1_11.5e1_author\&utm_campaign=Email_publication\&field=\&journalName=Frontiers_in_Psychology\&id=230461},
	author = {K. Agres and D. Herremans and L. Bigo and D. Conklin}
}
@proceedings {98,
	title = {Hit Song Prediction Based on Early Adopter Data and Audio Features},
	year = {2017},
	month = {10/2017},
	address = {Suzhou, China},
	author = {D. Herremans and T. Bergmans}
}
@proceedings {86,
	title = {IMMA-Emo: A Multimodal Interface for Visualising Score- and Audio-synchronised Emotion Annotations},
	year = {2017},
	month = {08/2017},
	address = {London, UK},
	author = {D. Herremans and S. Yang and C.-H. Chuan and M. Barthet and E. Chew}
}
@article {43,
	title = {A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music},
	journal = {International Transactions in Operational Research, Special Issue on Variable Neighbourhood Search},
	volume = {24},
	year = {2017},
	month = {05/2017},
	pages = {509{\textendash}535},
	abstract = {A piano fingering indicates which finger should play each note in a piece. Such a guideline is very helpful for both amateur and experienced players in order to play a piece fluently. In this paper, we propose a variable neighborhood search algorithm to generate piano fingerings for complex polyphonic music, a frequently encountered case that was ignored in previous research. The algorithm takes into account the biomechanical properties of the pianist{\textquoteright}s hand in order to generate a fingering that is user-specific and as easy to play as possible. An extensive statistical analysis was carried out in order to tune the parameters of the algorithm and evaluate its performance. The results of computational experiments show that the algorithm generates good fingerings that are very similar to those published in sheet music books.},
	doi = {10.1111/itor.12211},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/itor.12211/full},
	author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen}
}
@proceedings {67,
	title = {The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music},
	year = {2016},
	month = {07/2016},
	pages = {280-282},
	address = {San Francisco},
	isbn = {1-876346-65-5 },
	author = {K. Agres and L. Bigo and D. Herremans and D. Conklin}
}
@conference {82,
	title = {The effect of repetitive structure on enjoyment and altered states in uplifting trance music},
	booktitle = {2nd International Conference on Music and Consciousness (MUSCON 2), Brighton},
	year = {2015},
	author = {Agres, K and Bigo, L and Herremans, D and Conklin, D}
}
@inbook {35,
	title = {Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm},
	booktitle = {Mathematics and Computation in Music},
	series = {Lecture Notes in Computer Science},
	volume = {9110},
	year = {2015},
	pages = {149-160},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	keywords = {Combinatorial optimisation, Metaheuristics, OR in Music, Piano fingering, Tabu search},
	isbn = {978-3-319-20602-8},
	doi = {10.1007/978-3-319-20603-5_15},
	url = {http://dx.doi.org/10.1007/978-3-319-20603-5_15},
	author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen},
	editor = {Collins, Tom and Meredith, David and Volk, Anja}
}
